class_docstr: Variational fully-connected network. This is composed of an encode layer, reparameterization layer, and then a
  decode layer.
  
  Args:
      in_channels: number of input channels.
      out_channels: number of output channels.
      latent_size: number of latent variables to use.
      encode_channels: number of output channels for each hidden layer of the encode half.
      decode_channels: number of output channels for each hidden layer of the decode half.
      dropout: dropout ratio. Defaults to no dropout.
      act: activation type and arguments. Defaults to PReLU.
      bias: whether to have a bias term in linear units. Defaults to True.
      adn_ordering: order of operations in :py:class:`monai.networks.blocks.ADN`.
  
  Examples::
  
      # accepts inputs with 4 values, uses a latent space of 2 variables, and produces outputs of 3 values
      net = VarFullyConnectedNet(4, 3, 2, [5, 10], [10, 5])
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code: def _get_layer(
      self, in_channels: int, out_channels: int, bias: bool
  ) -> nn.Sequential:
      seq = nn.Sequential(nn.Linear(in_channels, out_channels, bias))
      seq.add_module("ADN", self.adn_layer)
      return seq
  
file_path: MONAI/monai/networks/nets/fullyconnectednet.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import torch
  import torch.nn
  from monai.networks.blocks import ADN
  from monai.networks.layers.factories import Act
label_desc: Use image node_id 2 for calling the VarFullyConnectedNet obj's underlying member method code with example usage: obj._get_layer(in_channels, out_channels, bias) and returns: seq